Shulou(Shulou.com)06/02 Report--

Recently, many people have asked the editor how they learn so much from big data. After many beginners came up with the idea of developing in the direction of big data, they could not help but have some questions about how to get started. Which techniques should be learned? What is the learning route? Today, the editor specially arranged a copy of big data's learning route from beginner to proficiency. Along with learning materials and videos. I hope I can help you all.

The first stage: Linux theory

(1) Linux basic; (2) Linux-shell programming; (3) High concurrency: lvs load balancing; (4) High availability & reverse proxy

The second stage: Hadoop theory

(1) hadoop-hdfs theory; (2) hadoop-hdfs cluster building; (3) hadoop-hdfs 2.x & api; (4) hadoop-MR theory

(5) hadoop-MR development analysis; (6) hadoop-MR source code analysis; (7) hadoop-MR development case

The third stage: Hive theory

(1) introduction and installation of Hive; (2) Hive practice

Phase IV: HBase

(1) introduction and installation of HBase; (2) HBase tuning

The fifth stage: redis theory

(1) redis type; (2) redis advanced

The sixth stage: Zookeeper theory

(1) introduction of Zookeeper; (2) use of Zookeeper

Phase 7: Scala grammar

(1) introduction of Scala grammar, (2) practical practice of scala grammar

The eighth stage: Spark theory

(1) Spark introduction; (2) Spark code development process; (3) Spark cluster building; (4) Spark resource scheduling principle

(5) Spark task scheduling; (6) Spark case; (7) two most important shuffle in Spark

(8) the construction of Spark high availability cluster; (9) SparkSQL introduction; (10) SparkSQL actual combat.

(11) SparkStreaming introduction; (12) SparkStreaming actual combat

Stage 9: introduction to machine learning

(1) detailed interpretation of linear regression; (2) logical regression classification algorithm; (3) Kmeans clustering algorithm; (4) KNN classification algorithm; (5) decision tree random forest algorithm

From zero foundation to project actual combat, real-time transaction monitoring system, recommendation system theory, database construction and so on. Partners who need the following big data learning materials can join the group for free and learn big data with industry Daniel.

The tenth stage: Elasticsearch theory

(1) Elasticsearch search principle, (2) Elasticsearch actual combat

Stage 11: Storm Theory

(1) Storm introduction and code practice; (2) Storm pseudo-distributed construction and task deployment; (3) detailed explanation of Storm architecture and DRCP principle

(4) Virtualization Theory kvm Virtualization; (5) docker

1Pol _ recommendation system theory and actual combat project Part2

2, recommendation system theory and actual combat project Part1

3. Real-time transaction Monitoring system Project (part two)

4. Real-time transaction monitoring system project (part I)

5, user behavior Analysis system Project 1

6, user behavior Analysis system Project 2

7. Detailed explanation of big data batch processing by HIVE

8pr es Open course part1

9,spark_streaming_

10. Detailed explanation of data warehouse construction

11, big data task scheduling

12, stream data integration artifact Kafka

13th Spark Open course

14, massive log collection weapon: Flume

A brief introduction to 15meme Impala

Brief introduction of 16 ~ (th) century Hive

A brief introduction to 17pr MapReduce

18 High-speed access to massive data database HBase

19, talking about the principle of Hadoop Manager yarn

2010, distributed full-text search engine ElasticSearch Part2

Conclusion: the above is big data's learning route from entry to proficiency, and there are many projects for everyone to practice. I wish you all every success in your work and promotion step by step!

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